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Real-life case studies * Life time access to Learning Management
System (LMS) * Practical Assignments * Certification: Global Tec
certifies you based on the project. * 24/7 customer support About
Data Science Certification Training
Global Tec’s Data Science course helps you gain expertise in Machine
Learning Algorithms like K-Means Clustering, Decision Trees, Random
Forest, Naive Bayes using R. You’ll learn the concepts of
Statistics, Time Series, Text Mining and an introduction to Deep
Learning. You’ll solve real life case studies on Media, Healthcare,
Social Media, Aviation, HR.
Who Should Apply?
The training is a best fit for:
* IT professionals interested in pursuing a career in analytics
* Graduates looking to build a career in analytics and data science
* Experienced professionals who would like to harness data science
in their fields
* Anyone with a genuine interest in the field of data science
Data Science Certification Training - Course Agenda
Introduction to Data Science
Goal – Get an introduction to Data Science in this Module and see
how Data Science helps to analyze large and unstructured data with
different tools.
Objectives – At the end of this Module, you should be able to:
• Define Data Science
• Discuss the era of Data Science
• Describe the Role of a Data Scientist
• Illustrate the Life cycle of Data Science
• List the Tools used in Data Science
• State what role Big Data and Hadoop, R, Spark and Machine Learning
play in Data Science
Topics:
• What is Data Science?
• What does Data Science involve?
• Era of Data Science
• Business Intelligence vs Data Science
• Life cycle of Data Science
• Tools of Data Science
• Introduction to Big Data and Hadoop
• Introduction to R
• Introduction to Spark
• Introduction to Machine Learning
Statistical Inference
Goal – In this Module, you should learn about different statistical
techniques and terminologies used in data analysis.
Objectives – At the end of this Module, you should be able to:
• Define Statistical Inference
• List the Terminologies of Statistics
• Illustrate the measures of Center and Spread
• Explain the concept of Probability
• State Probability Distributions
Topics:
• What is Statistical Inference?
• Terminologies of Statistics
• Measures of Centers
• Measures of Spread
• Probability
• Normal Distribution
• Binary Distribution
Data Extraction, Wrangling and Exploration
Goal – Discuss the different sources available to extract data,
arrange the data in structured form, analyze the data, and represent
the data in a graphical format.
Objectives – At the end of this Module, you should be able to:
• Discuss Data Acquisition techniques
• List the different types of Data
• Evaluate Input Data
• Explain the Data Wrangling techniques
• Discuss Data Exploration
Topics:
• Data Analysis Pipeline
• What is Data Extraction
• Types of Data
• Raw and Processed Data
• Data Wrangling
• Exploratory Data Analysis
• Visualization of Data
Hands-On/Demo:
• Loading different types of dataset in R
• Arranging the data
• Plotting the graphs
Introduction to Machine Learning
Goal – Get an introduction to Machine Learning as part of this
Module. You will discuss the various categories of Machine Learning
and implement Supervised Learning Algorithms.
Objectives – At the end of this module, you should be able to:
• Define Machine Learning
• Discuss Machine Learning Use cases
• List the categories of Machine Learning
• Illustrate Supervised Learning Algorithms
Topics:
• What is Machine Learning?
• Machine Learning Use-Cases
• Machine Learning Process Flow
• Machine Learning Categories
• Supervised Learning
* Linear Regression
* Logistic Regression
Hands-On/Demo:
• Implementing Linear Regression model in R
• Implementing Logistic Regression model in R
Classification
Goal – In this module, you should learn the Supervised Learning
Techniques and the implementation of various Techniques, for example,
Decision Trees, Random Forest Classifier etc.
Objectives – At the end of this module, you should be able to:
• Define Classification
• Explain different Types of Classifiers such as,
* Decision Tree
* Random Forest
* Naïve Bayes Classifier
* Support Vector Machine
Topics:
• What is Classification and its use cases?
• What is Decision Tree?
• Algorithm for Decision Tree Induction
• Creating a Perfect Decision Tree
• Confusion Matrix
• What is Random Forest?
• What is Navies Bayes?
• Support Vector Machine: Classification
Hands-On/Demo:
• Implementing Decision Tree model in R
• Implementing Linear Random Forest in R
• Implementing Navies Bayes model in R
• Implementing Support Vector Machine in R
Unsupervised Learning
Goal – Learn about Unsupervised Learning and the various types of
clustering that can be used to analyze the data.
Objectives – At the end of this module, you should be able to:
• Define Unsupervised Learning
• Discuss the following Cluster Analysis
* K – means Clustering
* C – means Clustering
* Hierarchical Clustering
Topics:
• What is Clustering & its Use Cases?
• What is K-means Clustering?
• What is C-means Clustering?
• What is Canopy Clustering?
• What is Hierarchical Clustering?
Hands-On/Demo:
• Implementing K-means Clustering in R
• Implementing C-means Clustering in R
• Implementing Hierarchical Clustering in R
Recommender Engines
Goal – In this module, you should learn about association rules and
different types of Recommender Engines.
Objectives – At the end of this module, you should be able to:
• Define Association Rules
• Define Recommendation Engine
• Discuss types of Recommendation Engines
* Collaborative Filtering
* Content-Based Filtering
• Illustrate steps to build a Recommendation Engine
Topics:
• What is Association Rules & its use cases?
• What is Recommendation Engine & it’s working?
• Types of Recommendation Types
• User-Based Recommendation
• Item-Based Recommendation
• Difference: User-Based and Item-Based Recommendation
• Recommendation Use-case
Hands-On/Demo:
• Implementing Association Rules in R
• Building a Recommendation Engine in R
Text Mining
Goal – Discuss Unsupervised Machine Learning Techniques and the
implementation of different algorithms, for example, TF-IDF and Cosine
Similarity in this Module.
Objectives – At the end of this module, you should be able to:
• Define Text Mining
• Discuss Text Mining Algorithms
* Bag of Words Approach
* Sentiment Analysis
Topics:
• The concepts of text-mining
• Use cases
• Text Mining Algorithms
• Quantifying text
• TF-IDF
• Beyond TF-IDF
Hands-On/Demo:
• Implementing Bag of Words approach in R
• Implementing Sentiment Analysis on twitter Data using R
Time Series
Goal – In this module, you should learn about Time Series data,
different component of Time Series data, Time Series modelling –
Exponential Smoothing models and ARIMA model for Time Series
forecasting.
Objectives – At the end of this module, you should be able to:
• Describe Time Series data
• Format your Time Series data
• List the different components of Time Series data
• Discuss different kind of Time Series scenarios
• Choose the model according to the Time series scenario
• Implement the model for forecasting
• Explain working and implementation of ARIMA model
• Illustrate the working and implementation of different ETS models
• Forecast the data using the respective model
Topics:
• What is Time Series data?
• Time Series variables
• Different components of Time Series data
• Visualize the data to identify Time Series Components
• Implement ARIMA model for forecasting
• Exponential smoothing models
• Identifying different time series scenario based on which
different Exponential Smoothing model can be applied
• Implement respective ETS model for forecasting
Hands-On/Demo:
• Visualizing and formatting Time Series data
• Plotting decomposed Time Series data plot
• Applying ARIMA and ETS model for Time Series forecasting
• Forecasting for given Time period
Deep Learning
Goal – Get introduced to the concepts of Reinforcement learning and
Deep learning in this Module. These concepts are explained with the
help of Use cases. You will get to discuss Artificial Neural Network,
the building blocks for artificial neural networks, and few artificial
neural network terminologies.
Objectives – At the end of this module, you should be able to:
• Define Reinforced Learning
• Discuss Reinforced Learning Use cases
• Define Deep Learning
• Understand Artificial Neural Network
• Discuss basic Building Blocks of Artificial Neural Network
• List the important Terminologies of ANN’s
Topics:
• Reinforced Learning
• Reinforcement learning Process Flow
• Reinforced Learning Use cases
• Deep Learning
• Biological Neural Networks
• Understand Artificial Neural Networks
• Building an Artificial Neural Network
• How ANN works
• Important Terminologies of ANN’s
Why Global Tec?
Global Tec's training is the best and value for time & money invested.
We stand out because our customers
* Get trained at the best price compared to other training
providers.
* Get trained by the best trainer in the industry.
* Get accesses to course specific learning videos.
* Get 100% Money back guarantee*.
Training Fee: $ 2499
Early Bird: Booking at least one month prior to the class start date
Training Venue:
Venue will be confirmed to the classroom participants one week prior
to the workshop start date and online participants will get the
session attendance link before 4- 5 days of the training start date.
Venue is finalized one week prior to the start date so that we can
accommodate last minute rescheduling from the participants and we do
not incur additional cost for rescheduling or cancellation.
For more details please contact US at +1 (832) 548 -0612 or
e-mail FRANK@GLOBALTEC-INC.COM
*We also provide the corporate training at any remote location, if you
have group
Participants. It can be conducted at your company premises on your
preferred dates.
*To know more about the discount and money back, Contact us on chat,
email or phone.
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26/09/2020 Last update